グリーンヘルスケアにおける持続可能な知能：低炭素臨床システムのためのエネルギー認識型AI、エッジコンピューティング、循環型デジタルインフラ

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:臨床AIのエネルギー効率と炭素会計を統合する測定枠組みの設計例として参照できる。

🏢実務担当者:医療機関・医療IT調達において、エッジ推論やカスケード構成による電力・炭素削減と調達基準見直しの根拠になる。

🏛政策担当者:医療部門の脱炭素とデジタル化を整合させる報告標準・調達政策・教育課程の論点を提供する。

Health systems are simultaneously a victim of climate change and a measurable contributor to it, accounting for roughly 4.4% of global net greenhouse gas emissions. Digital transformation is widely promoted as a decarbonisation lever for care delivery, yet the computational substrate on which digital health depends — data centres, network infrastructure, imaging workstations and an expanding population of connected devices — carries an energy and materials burden of its own that is rarely entered into the same ledger. This chapter argues that green healthcare and green computing must be engineered as a single optimisation problem rather than two adjacent agendas. It develops a four-layer reference architecture, the Green Health Informatics Stack, spanning instrumented sensing, carbon-aware connectivity, elastic and location-flexible computation, and a governance layer in which environmental cost is treated as a first-class non-functional requirement alongside accuracy, latency and privacy. Within this frame the chapter examines energy-aware model engineering for clinical artificial intelligence — quantisation, structured pruning, knowledge distillation, neural architecture search and early-exit inference — together with edge and federated deployment patterns that reduce data movement while preserving patient confidentiality, digital-twin-driven optimisation of hospital plant, and circular-economy strategies for medical electronics in a world generating 62 million tonnes of electronic waste annually. A measurement framework is proposed, comprising a carbon intensity per clinical inference metric, a diagnostic energy yield metric, and a composite clinical health-AI efficiency index, and is applied to an illustrative tiered arrhythmia-screening pipeline. The parametric analysis indicates that cascade architectures can plausibly reduce screening-phase inference energy by more than an order of magnitude relative to cloud-only deployment while retaining most of the diagnostic sensitivity, provided the early tiers operate at high recall. The chapter closes with an agenda for reporting standards, procurement policy and curriculum reform.Keywords: Circular economy, Clinical decision support, Digital twin, Edge

• openalex https://doi.org/10.5281/zenodo.22867815first seen 2026-09-23 05:00:55 · last seen 2026-09-23 05:01:09